Energy Efficient Downlink Resource Allocation in Cellular IoT Supported H-CRANs

Ferdouse, Lilatul; Woungang, Isaac; Anpalagan, Alagan; Erkucuk, Serhat

doi:10.1109/tvt.2021.3076825

Cited by 21 publications

(19 citation statements)

References 22 publications

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“…This work can be placed at the intersection of two research areas, that is, 1) dynamic resource allocation in C-RAN-based networks, 2) cooperative spectrum sensing in 5G networks. Radio resource allocation in C-RAN empowered networks attracts a lot of attention in the literature [17][18][19][20][21][22][23]. In [17], user association in C-RAN with massive MIMO configuration is studied to achieve high rates for cell-edge users.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, the objective is to maximize the average energy efficiency of the system under the constraints such as maximum power, average arrival rate and minimum data rate. Moreover, the problem of resource block assignment and power allocation with the aim of maximizing the energy efficiency under maximum power, fronthaul capacity and QoS constraints in cellular IoT supported heterogeneous CRAN is considered in [23].…”

Section: Related Workmentioning

confidence: 99%

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Spectrum sensing and resource allocation for 5G heterogeneous cloud radio access networks

et al. 2022

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In this paper, the problem of opportunistic spectrum sharing for the next generation of wireless systems empowered by the cloud radio access network (C-RAN) is studied. More precisely, low-priority users employ cooperative spectrum sensing to detect a vacant portion of the spectrum that is not currently used by high-priority users. The authors' aim is to maximize the overall throughput of the low-priority users while guaranteeing the quality of service of the high-priority users. This objective is attained by optimally adjusting spectrum sensing time, with respect to target probabilities of detection and false alarm, as well as dynamically allocating C-RAN resources, that is, powers, sub-carriers, remote radio heads, and base-band units. To solve this problem, which is non-convex and NP-hard, a low-complex iterative solution is proposed. Numerical results demonstrate the necessity of sensing time adjustment as well as effectiveness of the proposed solution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Spectrum sensing and resource allocation for 5G heterogeneous cloud radio access networks

et al. 2022

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“…21 Property 2. In the worst case, the constrained DA algorithm will have a complexity of O(M(N + F + D) 3 ). (Proof: See Appendix B).…”

Section: Definition 4 (Stable Matching)mentioning

confidence: 99%

“…Wireless communication has been an integral part of human society in recent years and covered many aspects of human life. Today, with the exponentially increasing of the mobile devices, the number of users, device‐to‐device (D2D) communication, 1,2 internet of things (IoT), 3,4 online games with high quality, social media, applications with high traffic consumption, and so on, the network traffic volume is also overgrowing. The main challenges may occur in dense areas with high connection density and very high mobility, such as office towers, subways, highways, railways, department stores, crowded residential areas, etc.…”

Section: Introductionmentioning

confidence: 99%

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

Shirmohamadi

Bakhshi

Dosaranian‐Moghadam

2022

Trans Emerging Tel Tech

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In this article, we propose an iterative algorithm based on lazy learning. The proposed scheme considers two consecutive algorithms for optimal resource allocation in heterogeneous cloud radio access networks (H-CRAN). In the conventional approach, resources allocation is usually presented as a mathematical optimization problem and solved online using a real-time scenario. Due to the non-convex nature of resource allocation problems, finding optimal solutions in real-time is a difficult challenge and has high computational complexity. To overcome these challenges in this paper, the problem is first formulated as a mixed-integer nonlinear programming (MINLP) to optimize the system capacity and assign the underlay users to the subchannels, which is NP-hard. To obtain the solution, in the first algorithm, the proposed problem is first reformulated as a convex problem using a matching theory and coalition formation method. Then, in the second algorithm, the lazy learning method is employed to find the optimal solution to the resources allocation problem. The lazy algorithm uses the output results of the first algorithm as training data, which are known as historical data. Similarities between current scenario data and historical scenario data, as well as the solution of resources allocation in historical scenarios, can be exploited to improve the resource allocation of the current scenario. Simulation results confirm that compared with the conventional approach, the suggested technique can significantly improve the system performance in terms of the total users assigned, overall throughput, the pace of resource allocation, and user fairness.

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“…With increased multimedia users and their consumed traffic, device-to-device communication (D2D), 1,2 Internet of Things (IoT), 3 existing mobile networks are overloaded. Therefore, the architecture of current mobile networks needs to be updated to increase their capacity.…”

Section: Introductionmentioning

confidence: 99%

User relay‐aided energy‐efficient resource allocation in heterogeneous cloud radio access network

Shirmohamadi

Bakhshi

Dosaranian‐Moghadam

2022

Trans Emerging Tel Tech

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In this article, we consider a user relay-aided resource allocation problem for femtocell access points (FAPs) and macrocells to improve energy efficiency in heterogeneous cloud radio access networks. The formulated optimization problem is NP-hard and mixed-integer non-linear programming due to the incredibly complex relation between different optimization variables and their impacts on each other. To solve this non-convex and NP-hard optimization problem in an effective and distributed method, we propose an efficient three-step iterative algorithm. The solution approaches are derived from successive convex approximation and complementary geometric programming by considering maximum power, front-haul link and baseband unit (BBU) capacity, Jain's fairness index, and quality of service constraints of macro and femtocell users. The simulation results depict that the proposed algorithm can effectively decrease the energy consumption of BBUs by switching off under-utilized BBUs and offloading the traffic from remote radio heads and macro-cell to FAPs.

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Energy Efficient Downlink Resource Allocation in Cellular IoT Supported H-CRANs

Cited by 21 publications

References 22 publications

Spectrum sensing and resource allocation for 5G heterogeneous cloud radio access networks

Spectrum sensing and resource allocation for 5G heterogeneous cloud radio access networks

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

User relay‐aided energy‐efficient resource allocation in heterogeneous cloud radio access network

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